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CS 558: Computer Vision 9Th Set of Notes 1 CS 558: Computer Vision 9th Set of Notes Instructor: Philippos Mordohai Webpage: www.cs.stevens.edu/~mordohai E-mail: [email protected] Office: Lieb 215 Introduction to object recognition By Svetlana Lazebnik Slides adapted from Fei-Fei Li, Rob Fergus, Antonio Torralba, and Jean Ponce Overview • Basic recognition tasks • A machine learning approach • Example features • Example classifiers • Levels of supervision • Datasets • Current trends and advanced recognition tasks Specific recognition tasks Scene categorization • outdoor/indoor • city/forest/factory/etc. Image annotation/tagging • street • people • building • mountain • … Object detection • find pedestrians Activity recognition • walking • shopping • rolling a cart • sitting • talking • … Image parsing sky mountain building tree building banner street lamp market people Image understanding? How many visual object categories are there? Biederman 1987 OBJECTS ANIMALS PLANTS INANIMATE NATURAL MAN-MADE ….. VERTEBRATE MAMMALS BIRDS TAPIR BOAR GROUSE CAMERA Recognition: A machine learning approach Slides adapted from Fei-Fei Li, Rob Fergus, Antonio Torralba, Kristen Grauman, and Derek Hoiem The machine learning framework • Apply a prediction function to a feature representation of the image to get the desired output: f( ) = “apple” f( ) = “tomato” f( ) = “cow” The machine learning framework y = f(x) output prediction function Image feature • Training: given a training set of labeled examples {(x1,y1), …, (xN,yN)}, estimate the prediction function f by minimizing the prediction error on the training set • Testing: apply f to a never before seen test example x and output the predicted value y = f(x) Steps Training Training Labels Training Images Image Learned Training Features model Learned model Testing Image Prediction Features Test Image Slide credit: D. Hoiem Generalization Training set (labels known) Test set (labels unknown) • How well does a learned model generalize from the data it was trained on to a new test set? Popular global image features • Raw pixels (and simple • Histograms, bags of features functions of raw pixels) • GIST descriptors [Oliva and • Histograms of oriented gradients Torralba, 2001] (HOG) [Dalal and Triggs, 2005] Classifiers: Nearest neighbor Training Training Test example examples examples from class 2 from class 1 f(x) = label of the training example nearest to x • All we need is a distance function for our inputs • No training required! Classifiers: Linear • Find a linear function to separate the classes: f(x) = sgn(w x + b) Recognition task and supervision • Images in the training set must be annotated with the “correct answer” that the model is expected to produce Contains a motorbike Unsupervised “Weakly” supervised Fully supervised Definition depends on task Datasets • Circa 2001: five categories, hundreds of images per category • Circa 2004: 101 categories • Today: tens of thousands of categories, millions of images Caltech 101 & 256 http://www.vision.caltech.edu/Image_Datasets/Caltech101/ http://www.vision.caltech.edu/Image_Datasets/Caltech256/ Griffin, Holub, Perona, 2007 Fei-Fei, Fergus, Perona, 2004 Caltech-101: Intra-class variability ImageNet http://www.image-net.org/ The PASCAL Visual Object Classes Challenge (2005-2012) http://pascallin.ecs.soton.ac.uk/challenges/VOC/ • Challenge classes: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor • Dataset size (by 2012): 11.5K training/validation images, 27K bounding boxes, 7K segmentations PASCAL competitions • Classification: For each of the twenty classes, predicting presence/absence of an example of that class in the test image • Detection: Predicting the bounding box and label of each object from the twenty target classes in the test image PASCAL competitions • Segmentation: Generating pixel-wise segmentations giving the class of the object visible at each pixel, or "background" otherwise • Person layout: Predicting the bounding box and label of each part of a person (head, hands, feet) PASCAL competitions • Action classification (10 action classes) LabelMe Dataset http://labelme.csail.mit.edu/ Russell, Torralba, Murphy, Freeman, 2008 SUN dataset ~900 scene categories (~400 well-sampled), 130K images J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba, "SUN Database: Large-scale Scene Recognition from Abbey to Zoo," CVPR 2010 http://groups.csail.mit.edu/vision/SUN/ Fine-grained recognition Source: J. Deng Fine-grained recognition … Cardigan Welsh Corgi ? … What breed is this dog? Pembroke Welsh Corgi Key: Find the right features. Source: J. Deng Geometric image interpretation V. Hedau, D. Hoiem, and D. Forsyth, Recovering the Spatial Layout of Cluttered Rooms, ICCV 2009. Geometric image interpretation A. Gupta, A. Efros and M. Hebert, Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics, ECCV 2010 Recognition from RGBD Images J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake, Real-Time Human Pose Recognition in Parts from a Single Depth Image, CVPR 2011 Attribute-based recognition A. Farhadi, I. Endres, D. Hoiem, and D Forsyth, Describing Objects by their Attributes, CVPR 2009 Attribute-based search A. Kovashka, D. Parikh and K. Grauman, WhittleSearch: Image Search with Relative Attribute Feedback, CVPR 2012 Face verification N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, Attribute and Simile Classifiers for Face Verification, ICCV 2009 Sentence generation from images G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. Berg, T. Berg, Baby Talk: Understanding and Generating Simple Image Descriptions, CVPR 2011.
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